A reversible fragment assembly method for de novo protein structure prediction Motivated by the protein structure prediction problem, we develop two variants of the Hamiltonian replica exchange methods ͑REMs͒ for efficient configuration sampling, ͑1͒ the scaled hydrophobicity REM and ͑2͒ the phantom chain REM, and compare their performance with the ordinary REM. We first point out that the ordinary REM has a shortage for the application to large systems such as biomolecules and that the Hamiltonian REM, an alternative formulation of the REM, can give a remedy for it. We then propose two examples of the Hamiltonian REM that are suitable for a coarse-grained protein model. ͑1͒ The scaled hydrophobicity REM prepares replicas that are characterized by various strengths of hydrophobic interaction. The strongest interaction that mimics aqueous solution environment makes proteins folding, while weakened hydrophobicity unfolds proteins as in organic solvent. Exchange between these environments enables proteins to escape from misfolded traps and accelerate conformational search. This resembles the roles of molecular chaperone that assist proteins to fold in vivo. ͑2͒ The phantom chain REM uses replicas that allow various degrees of atomic overlaps. By allowing atomic overlap in some of replicas, the peptide chain can cross over itself, which can accelerate conformation sampling. Using a coarse-gained model we developed, we compute equilibrium probability distributions for poly-alanine 16-mer and for a small protein by these REMs and compare the accuracy of the results. We see that the scaled hydrophobicity REM is the most efficient method among the three REMs studied.

Protein folding often competes with intermolecular aggregation, which in most cases irreversibly impairs protein function, as exemplified by the formation of inclusion bodies. Although it has been empirically determined that some proteins tend to aggregate, the relationship between the protein aggregation propensities and the primary sequences remains poorly understood. Here, we individually synthesized the entire ensemble of Escherichia coli proteins by using an in vitro reconstituted translation system and analyzed the aggregation propensities. Because the reconstituted translation system is chaperone-free, we could evaluate the inherent aggregation propensities of thousands of proteins in a translation-coupled manner. A histogram of the solubilities, based on data from 3,173 translated proteins, revealed a clear bimodal distribution, indicating that the aggregation propensities are not evenly distributed across a continuum. Instead, the proteins can be categorized into 2 groups, soluble and aggregation-prone proteins. The aggregation propensity is most prominently correlated with the structural classification of proteins, implying that the prediction of aggregation propensity requires structural information about the protein.cell-free translation ͉ protein aggregation ͉ protein folding

Allostery, the coupling between ligand binding and protein conformational change, is the heart of biological network and it has often been explained by two representative models, the inducedfit and the population-shift models. Here, we clarified for what systems one model fits better than the other by performing molecular simulations of coupled binding and conformational change. Based on the dynamic energy landscape view, we developed an implicit ligand-binding model combined with the doublebasin Hamiltonian that describes conformational change. From model simulations performed for a broad range of parameters, we uncovered that each of the two models has its own range of applicability, stronger and longer-ranged interaction between ligand and protein favors the induced-fit model, and weaker and shorter-ranged interaction leads to the population-shift model. We further postulate that the protein binding to small ligand tends to proceed via the population-shift model, whereas the protein docking to macromolecules such as DNA tends to fit the induced-fit model.allostery ͉ multiple-basin model ͉ coarse-grain model

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Biomolecules often undergo large-amplitude motions when they bind or release other molecules. Unlike macroscopic machines, these biomolecular machines can partially disassemble (unfold) and then reassemble (fold) during such transitions. Here we put forward a minimal structure-based model, the ''multiple-basin model,'' that can directly be used for molecular dynamics simulation of even very large biomolecular systems so long as the endpoints of the conformational change are known. We investigate the model by simulating large-scale motions of four proteins: glutamine-binding protein, S100A6, dihydrofolate reductase, and HIV-1 protease. The mechanisms of conformational transition depend on the protein basin topologies and change with temperature near the folding transition. The conformational transition rate varies linearly with driving force over a fairly large range. This linearity appears to be a consequence of partial unfolding during the conformational transition.conformational transition ͉ cracking ͉ partial unfolding ͉ funnel T o function, biomolecules often undergo large-amplitude structural changes upon binding or releasing ligands. These structural changes organize the workings of biomolecular machines such as the ribosome, molecular chaperones, and molecular motors. Structural information on the conformational ensembles before and after the conformation changes is often available through x-ray crystallography or NMR. These experiments, however, provide primarily quasistatic information. They reveal directly less about the transition dynamics between two end structures. The overall dynamics of basin-hopping can be studied by pump-probe experiments or from NMR relaxation. These experiments, however, usually monitor directly only a few local structure changes in what is typically a huge system. Thus, we see that global time-dependent structural information at high resolution is rarely obtained directly by experiments. Simulations can potentially provide full timedependent structural information on biomolecular machines. Yet conventional atomistic simulations currently only reach times up to microseconds (1). This time scale falls orders of magnitude short of the typical physiologically important time scales of milliseconds to seconds. To overcome this limitation, one approach is to coarsegrain the molecular representation (2). Reduction in complexity allows one to simulate much longer times. This so-called ''minimalist approach'' has been quite successful for studying protein folding (3-8). The purpose of this article is to investigate a minimal structure-based model, which we call the ''multiple-basin model,'' to simulate large-scale conformational changes when structures for the endpoints of the transition are available. This approach can be used for simulations of even very large biomolecular complexes.To motivate the present model, we first note that two qualitatively different kinds of protein motions occur depending on the amplitude of motion. For small deviations from the fiducial native structure m...